# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import mindspore.context as context
import mindspore.nn as nn
import numpy as np
import pytest
from mindspore.common.api import jit
from mindspore.ops.composite import GradOperation

from mindspore import Tensor
from mindspore.ops import operations as P
from tests.mark_utils import arg_mark

context.set_context(mode=context.GRAPH_MODE, device_target='CPU')


class Grad(nn.Cell):
    def __init__(self, network):
        super(Grad, self).__init__()
        self.grad = GradOperation(get_all=True, sens_param=True)
        self.network = network

    @jit
    def construct(self, input_, output_grad):
        return self.grad(self.network)(input_, output_grad)


class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.ops = P.Neg()

    def construct(self, x):
        return self.ops(x)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_net():
    x = np.random.randn(2, 3, 3, 4).astype(np.float32)
    y_expect = -x
    net = Net()
    out = net(Tensor(x))
    assert (out.asnumpy() == y_expect).all()
    sens = np.random.randn(2, 3, 3, 4).astype(np.float32)
    backword_net = Grad(Net())
    output = backword_net(Tensor(x), Tensor(sens))
    print(len(output))
    print(output[0].asnumpy())


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
@pytest.mark.parametrize('dtype', [np.uint8, np.uint16, np.uint32, np.uint64,
                                   np.int8, np.int16, np.int32, np.int64,
                                   np.float16, np.float32, np.float64,
                                   np.complex64, np.complex128])
def test_neg_tensor_api_modes(mode, dtype):
    """
    Feature: Test neg tensor api.
    Description: Test neg tensor api for Graph and PyNative modes.
    Expectation: The result match to the expect value.
    """
    context.set_context(mode=mode, device_target="CPU")
    x_np = np.array([1, 2, -1, 2, 0, -5], dtype=dtype)
    output_np = np.negative(x_np)
    x = Tensor(x_np)
    output = x.neg()
    np.testing.assert_array_equal(output.asnumpy(), output_np)
